),
        )
        # Now generate the embeddings for all the texts
        embeddings = embedding.embed_documents(texts)
        client.upsert(
            collection_name=collection_name,
            points=rest.Batch(
                ids=[uuid.uuid4().hex for _ in texts],
                vectors=embeddings,
                payloads=cls._build_payloads(
                    texts, metadatas, content_payload_key, metadata_payload_key
                ),
            ),
        )
        return cls(
            client=client,
            collection_name=collection_name,
            embedding_function=embedding.embed_query,
            content_payload_key=content_payload_key,
            metadata_payload_key=metadata_payload_key,
        )
    @classmethod
    def _build_payloads(
        cls,
        texts: Iterable[str],
        metadatas: Optional[List[dict]],
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> List[dict]:
        payloads = []
        for i, text in enumerate(texts):
            if text is None:
                raise ValueError(
                    "At least one of the texts is None. Please remove it before "
                    "calling .from_texts or .add_texts on Qdrant instance."
                )
            metadata = metadatas[i] if metadatas is not None else None
            payloads.append(
                {
                    content_payload_key: text,
                    metadata_payload_key: metadata,
                }
            )
        return payloads
    @classmethod
    def _document_from_scored_point(
        cls,
        scored_point: Any,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document:
        return Document(
            page_content=scored_point.payload.get(content_payload_key),